Stochastic closed-loop model predictive control of continuous nonlinear chemical processes

✍ Scribed by Dennis Van Hessem; Okko Bosgra

• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 345 KB
• Volume: 16
• Category: Article
• ISSN: 0959-1524
• DOI: 10.1016/j.jprocont.2005.06.003

✦ Synopsis

A new predictive control framework for chemical processes is presented, that has a number of fundamental differences to classical MPC. Both future disturbances and future process measurements are explicitly introduced in the model prediction, while back-off prevents violation of the inequality constraints. A feedforward trajectory, used for constraint pushing, is optimized simultaneously with a linear time-varying feedback controller, used to minimize the back-off. No feedback is generated by the receding horizon implementation itself. Via several transformations, the resulting optimization problem is rendered convex. For nonlinear processes, this applies to the sub-problem in a sequential conic optimization approach. A two stage LQG approach reduces the complexity even further for large scale systems. The method is illustrated on a HDPE reactor example and compared to a LTV-MPC.